3D Slicer as an image computing platform for the Quantitative Imaging Network.

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

/pdf/the-multimodal-brain-tumor-image-segmentation-benchmark-2gfraj98dk.pdf

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

https://spiral.imperial.ac.uk/bitstream/10044/1/53839/13/1-s2.0-S1361841516301839-main.pdf

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

Brain tumor segmentation with Deep Neural Networks

NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain

http://midag.cs.unc.edu/pubs/papers/Neuroimage06_Yushkevich.pdf

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

https://www.cs.unc.edu/Research/MIDAG/pubs/papers/MEDIA04-Prastawa-tumor.pdf

A brain tumor segmentation framework based on outlier detection.

We develop a new method for automatic segmentation of anatomical structures from volumetric medical images. Driving application is tumor segmentation from 3-D MRIs, which is known to be a very challenging problem due to the variability of tumor geometry and intensity patterns. Level-set snakes offer significant advantages over conventional statistical classification and mathematical morphology, however snakes with constant propagation need careful initialization and can leak through weak or missing boundary parts. Our region competition method overcomes these problems by modulating the propagation term with a signed local statistical force, leading to a stable solution. A pre- vs. post-contrast difference image is used to calculate probabilities for background and tumor regions, with a mixture-modelling fit of the histogram. Preliminary results on five cases with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert segmentation.

/pdf/level-set-evolution-with-region-competition-automatic-3-d-2owbg4r19g.pdf

Level-set evolution with region competition: automatic 3-D segmentation of brain tumors

Given models for healthy brains, tumor segmentation can be seen as a process of detecting abnormalities or outliers that are present with certain image intensity and geometric properties. In this paper, we propose a method that segments brain tumor and edema in two stages. We first detect intensity outliers using robust estimation of the location and dispersion of the normal brain tissue intensity clusters. We then apply geometric and spatial constraints to the detected abnormalities or outliers. Previously published tumor segmentation methods generally rely on the intensity enhancement in the T1-weighted image that appear with the gadolinium contrast agent, on strictly uniform intensity patterns and most often on user initialization of the segmentation. To our knowledge, none of the methods integrated the detection of edema in addition to tumor as a combined approach, although knowledge of the extent of edema is critical for planning and treatment. Our method relies on the information provided by the (non-enhancing) T1 and T2 image channels, the use of a registered probabilistic brain atlas as a spatial prior, and the use of a shape prior for the tumor/edema region. The result is an efficient, automatic segmentation method that defines both, tumor and edema.

/pdf/robust-estimation-for-brain-tumor-segmentation-2g1nt7pzb7.pdf

Robust estimation for brain tumor segmentation

We present a new software package for interactive segmen- tation of 3D images by geodesic snakes, with manual editing. Several variants of level set snakes are incorporated, including both gradient- magnitude based snakes as well as region-competition snakes. A complete segmentation pipeline, including image preprocessing, bubble initializa- tion and parameter control, is provided in an intuitive user interface. Slice-based overpainting is also provided in the same framework, for man- ual editing of the automatic snake segmentation. This tool is already in daily use by clinicians and oers a vast improvement in speed over tradi- tional manual segmentation. We present examples from several clinical studies, as well as validation results comparing the automatic geodesic snakes segmentation with manual expert rater segmentation.

/pdf/snap-a-software-package-for-user-guided-geodesic-snake-40d93gy1we.pdf

Sean Ho

Papers

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

A brain tumor segmentation framework based on outlier detection.

Level-set evolution with region competition: automatic 3-D segmentation of brain tumors

Robust estimation for brain tumor segmentation

SNAP: A Software Package for User-Guided Geodesic Snake Segmentation